Prof. Zhaoxia Guo, Sichuan University, China
Zhaoxia Guo received his PhD degree from The Hong Kong Polytechnic University in 2008. He is currently a full professor and the head of the Department of Industrial Engineering and Management at Business School of Sichuan University, China. His recent research interests include artificial intelligence and applications, complex systems modelling and management, and data-driven decision-making. He has published a textbook titled as Intelligent Algorithms: Principles and Applications, three monographs that focus on AI applications in operations managements, and over 100 research papers in refereed journals and international conference proceedings, such as One Earth, Nature Communications, INFORMS Journal on Computing, IISE Transactions, Science Bulletin, Scientific Data, International Journal of Geographical Information Science, Transportation Research Part C-E, and IEEE Transactions. He has received numerous awards, including the 4th place in the 2024 "Data Elements ×" National Competition Finals (Urban Governance Track), the 2023 China Annual Best Paper Award from Cell Press, the Second Prize of the Sichuan Provincial Science and Technology Progress Award in 2017, the Gold Award for Best Innovative Application at the 2012 Hong Kong RFID Awards, and the Second Place of the IMB 2009 Technology Innovation Award sponsored by the European Commission. He was selected an academic and technical leader in Sichuan Province, and has held leadership roles (e.g., deputy general conference chair, organizing committee chair, and cluster chair) in multiple international conferences related to artificial intelligence, industrial engineering, and operations research.
Speech Title: Neural combinatorial optimization for practical vehicle routing problems: Advances and challenges
Abstract: Neural combinatorial optimization (NCO) has emerged as a promising paradigm for solving combinatorial optimization problems by leveraging the power of deep learning. Unlike traditional optimization techniques, NCO offers a data-driven, and adaptive approach that learns from problem instances and demonstrates strong generalization capabilities for effectively handling a wide range of problem instances. In recent years, there has been an increasing interest in developing NCO models to tackle routing problems. This talk focuses on the applications of NCO in solving practical vehicle routing problems (VRPs), which are fundamental in logistics and transportation. Several representative NCO models for VRPs will be reviewed firstly. I will then highlight how we developed NCO models to address VRPs with complex real-world constraints, such as time-dependent travel speeds and uncertain parking availability, which are challenging for traditional techniques. The results of performance comparisons between our NCO models and several benchmark models will be presented. Finally, significant challenges in applying NCO to practical VRPs will be discussed.
Prof. Kin Choong Yow, University of Regina, Canada
Kin-Choong Yow obtained his B.Eng (Elect) with 1st Class Honours from the National University of Singapore in 1993, and his Ph.D. from Cambridge University, UK in 1998. He joined the University of Regina in September 2018, where he is presently a Professor in the Faculty of Engineering and Applied Science. Prior to joining UofR, he was an Associate Professor in the Gwangju Institute of Science and Technology (GIST), Republic of Korea, (2013-2018), Professor at the Shenzhen Institutes of Advanced Technology (SIAT), P.R. China (2012-2013), and Associate Professor at the Nanyang Technological University (NTU), Singapore (1998-2013). In 1999-2005, he served as the Sub-Dean of Computer Engineering in NTU, and in 2006-2008, he served as the Associate Dean of Admissions in NTU. Kin-Choong Yow’s research interest is in Artificial General Intelligence and Smart Environments. Artificial General Intelligence (AGI) is a higher form of Machine Intelligence (or Artificial Intelligence) where the intelligent agent (or machine) is able to successfully perform any intellectual task that a human being can. Kin-Choong Yow has published over 100 top quality international journal and conference papers, and he has served as reviewer for a number of premier journals and conferences, including the IEEE Wireless Communications and the IEEE Transactions on Education. He has been invited to give presentations at various scientific meetings and workshops, such as ACIRS, in 2018 and 2019; ICSPIC, in 2018; and ICATME, in 2021. He is the Editor-in-Chief of the Journal of Advances in Information Technology (JAIT), a Managing Editor of the International Journal of Information Technology (IntJIT), and a Guest Editor of MDPI Applied Sciences. He is also a member of APEGS and ACM, and a senior member with the IEEE. His pioneering work in Mobile and Interactive Learning won the HP Philanthropy grant in 2003 for applying Mobile Technologies in a Learning Environment. Only 7 awards were given to the 21 Asia Pacific Countries who were invited, and his project was the only one from Singapore to win it. Also, in 2003, he was one of the only 2 Singaporeans to be awarded participation to the ASEAN Technology Program on Multi Robot Cooperation Development held in KAIST, Korea. He was the winner of the NTU Excellence in Teaching Award 2005, and he won the Most Popular SCE Year 1 lecturer for 4 consecutive years 2004-2007. He has led numerous student teams to National and International victories such as the IEEE Computer Society International Design Competition (CSIDC) (2001), the Microsoft Imagine Cup (2002, 2003 and 2005), and the Wireless Challenge (2003).
Dr. Nikhil Patel, Deloitte Consulting LLP, USA
Speech Title: CVS: A Novel Framework for Personality-based Automatic CV Sorting using Deep Learning
Abstract—Both skill and personality play impactful roles in professional performance. The Human Resource Management (HRM) identiffes and veriffes the skill set and academic background while recruiting new employees. However, analyzing the personalities of the applicants is challenging. Because humans have intrinsic characteristics that allow them to express fabricated personalities in different settings. Nevertheless, people frequently express their true sentiments on social media. This presentation will present an innovative and effective framework, the Curriculum Vitae Sorting (CVS) framework, that uses Bidirectional Long Short-Term Memory (BiLSTM) and the Myers-Briggs Type Indicator (MBTI) dataset to identify the personalities of job applicants using their social media posts. The CVS framework achieves a remarkable 92.88% classiffcation accuracy with a 4.55% False Positive Rate (FPR). The practical application of this framework demonstrates an 11.67% improvement in the Key Performance Indicator (KPI) among newly recruited employees. The 93.11% precision, 92.94% recall, and 94.03% F1-score of the CVS framework demonstrate its outstanding and reliable performance in personality classiffcation. This unique application of Deep Learning (DL) in HRM unearths a new dimension of Artiffcial Intelligence (AI) in business, helping organizations recruit employees with the required personalities and qualities. Index Terms—Personality Classiffcation, CV Sorting, BiLSTM Network, Myers-Briggs Type Indicator, Deep Learning, Neural Network, Natural Language Processing.